Optimal Parameter Combination Research Articles

Optimization of Flexural Performance of PETG Samples Produced by Fused Filament Fabrication with Response Surface Method.

Additive manufacturing (AM), particularly fused filament fabrication (FFF), has gained significant attention for its design flexibility and cost-effectiveness. This study focuses on optimizing FFF parameters that employ response surface methodology (RSM) to enhance the flexural performance of polyethylene terephthalate glycol (PETG) parts. Three essential parameters-layer height, print speed, and nozzle temperature-were varied, and their effects on flexural strength, flexural modulus, flexural toughness for ultimate strength, flexural toughness at 5% strain, and strain at ultimate strength were evaluated. Based on a Box-Behnken design, the experiments revealed significant effects of these parameters on the mechanical responses. The analysis of variance (ANOVA) indicates that layer height predominantly affects flexural modulus and toughness, while nozzle temperature significantly impacts flexural strength. The RSM models exhibited high accuracy, with R2 values exceeding 99%. Optimal parameter combinations yield remarkable improvements: flexural strength reached 39.55 MPa, flexural modulus peaked at 1344.60 MPa, flexural toughness for ultimate strength reached 218.22 J/mm3, flexural toughness at 5% strain reached 381.47 J/mm3, and strain at ultimate strength reached 3.50%. Validation experiments confirm the effectiveness of the optimization, with errors below 3.17%.

Optimal design of an inclined barrier for performance improvement of an oscillating water column device with an artificial neural network model

An oscillating water column (OWC) device with a surface-piercing inclined barrier has been studied in irregular waves. This study is to determine the optimal shape of an inclined barrier utilizing an Artificial Neural Network (ANN) model for the performance enhancement of the conventional OWC system. We employed the dual boundary element method (DBEM) as our numerical approach, which resolves two separate boundary integral equations (singular and hyper-singular) formulated at the coincident source points situated on either side of the degenerate barrier boundary. The design parameters affecting the performance of the OWC system have been optimized to maximize the power extraction using the trained ANN model. The training dataset was prepared using the DBEM, which is then used to train and validate the ANN model. Subsequently, the trained model was employed to make predictions using a broader dataset to identify the optimal combination of design parameters that maximize the conversion efficiency. The findings reveal that the well-trained ANN model can learn complex nonlinear relationships, make good predictions with high accuracy using a larger dataset of input conditions, and offer the optimal values of an inclined barrier appropriate for the wave conditions at the sea installation site.

Prediction of the swelling rate of irradiated type 316 stainless steels via machine learning methods

Irradiation-induced void swelling typically leads to the degradation of mechanical properties of metals and the significant decrease in material density, which influences the safety of nuclear reactors. It is essential to understand the irradiation-induced swelling behavior of alloys. In this work, machine learning (ML) methods are adopted to make a prediction on the swelling rate of type 316 stainless steels. Based on the defined target variable and potentially influential factors, the dataset which contains 333 samples and 23 features is created with excavating previous studies. The correlation analysis between all pairs of variables is performed firstly. Based on the grid search technology, k-fold cross validation is used to obtain the optimal combination of parameters for ML models. Five ML models are fitted and their predictive performances are compared. Because GradientBoosting (GB) model exhibits the best prediction, it is used to identify the most controlling factors in the prediction of the irradiation-induced swelling behavior of alloys. The GB model gives the results which are in agreement with the established conventional understanding of factors affecting the swelling rate of type 316 stainless steels. As a summary, the application of ML to the prediction of the irradiation-induced swelling behavior of alloys provides a new insight to the development of irradiation resistance steels.

Effects of transcranial direct current stimulation on different cognitive domains in Alzheimer's disease: a meta-study.

Numerous studies have investigated the potential effects of transcranial direct current stimulation (tDCS) on improving symptoms related to Alzheimer's disease (AD). However, these studies have produced inconsistent results, leading to a need for further investigation. A comprehensive search was conducted, including articles published from the initial availability date to 5 April 2024. The extracted study data were analyzed using STATA 12.0 software. The standard mean difference (SMD) and a 95% confidence interval (CI) were calculated to assess the effects of tDCS. A total of 18 studies assessing the effects of tDCS on AD were included in the study. The study revealed that tDCS has an immediate positive impact on general cognitive, executive, language, and visuospatial function. However, the study did not observe any other significant effect of tDCS treatment on improvements in brain function, including long-term effects on general cognitive, attention, language, and memory function, as well as immediate effects on attention and memory function. In conclusion, the study suggests that tDCS may be a promising intervention for improving the cognitive function of patients with AD. However, given the complex and multifactorial nature of AD, further well-designed studies with larger sample sizes are necessary to clarify the effectiveness of tDCS and determine the optimal combination of tDCS parameters.

Investigation of continuous wave/coaxial waterjet-assisted laser combined machining of micro holes in GH3536

A two-step combined processing technique, combining a continuous-wave laser with a waterjet-assisted nanosecond laser, has been introduced to enhance the quality and efficiency of laser micro-hole processing for nickel-based superalloys. Initially, a through-hole was created using a continuous-wave laser, followed by hole refinement with a coaxial waterjet-assisted nanosecond laser. In the first step, orthogonal testing methods were employed to investigate the impact of various parameters, including power, duty ratio, frequency, defocusing amount, and processing time, on the entrance and exit apertures, as well as the hole taper. Through the analysis of variance (ANOVA) method, the optimal parameter combination A2B1C4D3E1 was identified, achieving the minimal hole taper. This combination comprised a power level of 80 %, a duty ratio of 15 %, a frequency of 400 Hz, a defocusing amount of 2 mm, and a processing time of 10 ms. In the second step, a comparison was made between scanning and trepan drilling machining methods. The results unequivocally demonstrated that scanning drilling achieves superior entrance and exit morphologies. Furthermore, the effect of water jet velocity on the morphology of micropores was thoroughly analyzed. The voltage range of the water pump was adjusted from 10 V to 24 V, revealing that a voltage of 20 V produced the smallest hole taper. The scanning filling diameter was accurately preset according to the preformed hole aperture as well as the thickness of the recast layer. Finally, after coaxial waterjet-assisted laser precision machining, the inner wall of the hole is free of cracks, and the recast layer and oxide layer are effectively minimized. The initial hole prefabrication with the continuous-wave laser took merely 0.01 s, and the subsequent fine-tuning with the coaxial waterjet-assisted laser took 2 min and 25 s.

Numerical Simulation and Bench Test of Crawler-Type Cotton Time-Delay Hole-Forming Device Based on RecurDyn-EDEM Coupling

In view of the challenges faced by cotton dibbler in Xinjiang under high-speed operation, a novel crawler-type delayed hole-forming device has been designed to address the seed throwing and floating issues in high-speed cotton dibbling in Xinjiang, enhancing the duck bill’s performance. This mechanism increases the sowing speed to 6 km/h by extending the duck bill horizontally. Utilizing agronomic principles, the mechanism’s layout and key components were optimized for efficient hole-forming. DEM and multi-body dynamics simulations were employed to analyze the motion, focusing on the fixed the tilt angle of the duck bill (A), the depth of the duck bill hole-forming into the soil (B), and the angle of rotation of the moving duck bill (C) as factors affecting hole dimensions (longitudinal length of hole Y1 and hole-forming depth of cotton seed hole Y2). Quadratic regression test using RecurDyn-EDEM coupling identified optimal parameter settings for maximum hole-forming performance. When A was 2.4°, B was 42.4 mm, and C was 30.5°, the performance of the hole-forming was the best. Under the optimal parameter combination, the bench verification test was carried out. The error between the bench verification results and the simulation results is small, indicating that the model has high accuracy. The average opening time of the duck bill at a speed of 6 km/h is 0.45 s, which is much longer than the time required for cotton seeds to fall from the duck bill (0.11 s). It meets the requirements of high-speed cotton planting in China and facilitating advancements high-speed planter technology.

Integrated scheduling of multi-objective lot-streaming hybrid flowshop with AGV based on deep reinforcement learning

The conventional approach to shop floor scheduling often overlooks handling time, either neglecting it or assimilating it into processing time. This results in inadequate information sharing, hindering operational synergy and impeding overall optimisation. Addressing this issue, this study focuses on the three-stage lot-streaming hybrid flowshop scheduling problem with automated guided vehicles (LSHFSP-AGV). It introduces a multi-objective scheduling model to enhance collaboration between machine production and AGV distribution, three objectives of minimum maximum completion time, minimum total machine idle time, and shortest AGV transportation distance are set. An improved multi-objective double-depth Q learning algorithm (NSGA2-MDDQN) based on NSGA-II is proposed to solve the problem. Taguchi experiments were conducted to determine the optimal parameter combination among alternative parameter combinations, and extensive numerical experiments involving 27 instances demonstrated the superiority of NSGA2-MDDQN over combinatorial scheduling rules, MDDQN, and NSGA-II, proving the superiority of the algorithm. The experimental results show that on the objective of minimising makespan, NSGA2-MDDQN reduces by an average of 23.17% compared to the composite scheduling rule, NSGA-II and MDDQN. And achieving an average reduction of 43.78% in machine idle time, and 9.12% in AGV transport distance.

Discrete Element Model of Oil Peony Seeds and the Calibration of Its Parameters

Oil peony is an important oil crop that is primarily sown by using artificial methods at present. Its seeder encounters the problems of low efficiency of seeding that significantly limits the highly efficient mechanized production of high-quality peony oil. In this study, Fengdan white oil peony seeds were used as the research object and repose angle as the response value to establish a discrete element model (DEM) and parameter calibration. The range of parameters of oil peony seeds was first obtained through an experiment, and their repose angle was obtained by an inclinometer. A three-dimensional DEM of oil peony seeds was then established. The Plackett–Burman (PB) test was utilized to screen the parameters that had a significant influence on the repose angle, and the steepest ascent (SA) test was applied to determine their optimum range of testing. Following this, based on Box–Behnken (BBD) test results, a second-order regression model between the important parameters and the repose angle was constructed. Finally, the absolute minimum difference between simulated and measured repose angles was utilized as the objective of optimization to obtain the following optimum combination of parameters: The values of the seed–steel collision recovery coefficient (CRC), seed–seed static friction coefficient (SFC), seed–steel SFC, and seed–seed rolling friction coefficient (RFC) were 0.704, 0.324, 0.335, and 0.045, respectively. This optimal combination of parameters was confirmed through simulations, and the error between simulated and measured repose angles was only 0.67%, indicating that the calibrated DEM of oil peony seeds was reliable.

Impact of inclined magnetic field on natural convection in ferrofluid within a semi-circular enclosure with a heated wavy surface

Ferrofluids are liquids that exhibit distinctive magnetic properties when subjected to a magnetic field. Due to their magnetic susceptibility, ferrofluids have recently gained considerable scientific attention. They find tools in different fields, such as electronics, mechanical engineering, and materials science. The inclusion of the semi-circular enclosure inscribed around a heated wavy cylinder provides innovative perspectives essential for engineering applications involving induction furnaces and cancer therapy devices. This paper aims to conduct a computational investigation into the influence of an inclined magnetic field on the free convection within a semi-circular cage stuffed with a ferrofluid featuring an internally undulating surface. It is considered that the interior wavy cylinder was warmed ( T h ) , and the outer semi-circular enclosure is cold ( T c ) . The analyzed system’s governing equations are computed using the Galerkin finite element method (GFEM). The ferrofluid’s flow pattern and thermal transport rate are investigated about relevant non-dimensional values, such as the wave numbers, the number of Hartmann ( 0 ≤ Ha ≤ 20 ) , the number of Rayleigh ( 10 3 ≤ Ra ≤ 10 6 ) , and the volume fraction ( 0.00 ≤ ϕ ≤ 0.09 ) . Evaluating the results involves examining isotherms, analyzing the distribution of flow velocities, and calculating the Nu avg , all while considering variations in fundamental physical parameters. As the Hartmann number ( Ha ) decreases and both the number of Rayleigh ( Ra ) and volume fraction ( ϕ ) rises, the Nu avg of the ferrofluid exhibits an increased. As a result of incorporating ferroparticles, the simulation identifies a maximum value of 35.86 for the Nu avg . It is worth noting that an enhance in Hartmann numbers is associated with an 80% decrease in the velocity stream function. On the other hand, an escalation in ferroparticle values leads to a 9.7% reduction, while elevated Rayleigh numbers result in a 60.8% surge in the stream function. Moreover, the irreversibility stemming from fluid friction, transfer of heat, and the magnetic field can be minimized for ferrofluid fluids ( n = 1 ), by employing the optimal parametric combination.

Parameter optimization of the classifying device of straw micro-crusher based on neural network and CFD

To improve the classification performance of the straw micro-crusher's classifying device, the characteristic parameters impacting classification performance are identified through an analysis of the forces acting on the straw particles between the blades of the rotor cage and the motion characteristics of the fluid. Taguchi's experimental design is used to ascertain the combinations of computational fluid dynamics (CFD) simulation parameters for cut size and classifying sharpness index, while a neural network (NN) model is developed using CFD data to predict the optimal feature parameter combination for classification performance. The results reveal that the rotor cage speed holds the most significant impact on both the cut size and classifying sharpness index. The optimal combination of feature parameters recommended by the neural network is v=8m/s, n=1200r/min, z=36, θ=40°, at which the cut size is 28.3 μm, a reduction of 9.0 % compared to the Taguchi experiment. In addition, the relative error between the cut size predicted by the neural network model and the CFD simulation is less than 4.5 %, indicating the reliability of the neural network model in predicting the optimal parameter combination of the classifying device.

Prediction of effective parameters for 3D printing of poly lactic acid-carbon fibre composites using intelligent frameworks based on mechanical response

Purpose This paper aims to find the effective 3D printing process parameters based on mechanical characteristics such as tensile strength and hardness of poly lactic acid (PLA)/carbon fibre composites (CF-PLA) by implementing intelligent frameworks. Design/methodology/approach The experiment trials are conducted based on design of experiments (DoE) using Taguchi L9 orthogonal array with three factors (speed, infill % and pattern type) and three levels. The factors have been optimized by solving the regression equation which is obtained from analysis of variance (ANOVA). The contour plots are generated by response surface methodology (RSM). The influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness. Findings The influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness. The results obtained from RSM are also confirmed by implementing the machine learning classifiers, such as logistic regression, ridge classifier, random forest, K nearest neighbour and support vector classifier (SVC). The results show that the SVC can predict the optimized process parameters with an accuracy of 95.65%. Originality/value 3D printing parameters which are considered in this work such as pattern types for PLA/CF-PLA composites based on intelligent frameworks has not been attempted previously.

Effect of bionic texture on the lubrication efficiency and mechanical efficiency loss for rotating gears

In order to enhance lubrication effectiveness and transmission efficiency in gear transmission, it is imperative to minimize mechanical efficiency losses and frictional wear of the gears during the lubrication process. This paper proposes a bionic design scheme for the tooth surface structure of gears based on the surface texture of bay scallop shells, considering the operational conditions within the gearbox. Firstly, the microstructure of the bay scallop shell surface is analyzed, and a bionic gear mapping model based on the bay scallop shell surface is established. Meanwhile, the oil coverage rate and convective heat transfer coefficient of gear surfaces with different textures was analyzed using finite element analysis. The results showed that the oil coverage rate of gear tooth surfaces with bionic fringes surpassed that of conventional gear lubrication. Thirdly, based on the jet lubrication test calculation, it is proposed that the bionic gear exhibits a lower mechanical efficiency loss and wear mass compared to conventional gears, while the mechanical efficiency loss and wear mass of arc groove gear type lower than that of vertical groove gears. Finally, the optimal structure of the arc groove gear was predicted through orthogonal data analysis, and the validity of the data prediction was verified through experiments and simulations. The optimal combination of texture parameters for the arc groove gear is as follows: a texture depth of 225 μm, a texture width of 275 μm, a texture interval of 275 μm, and a texture length of 1600 μm. As a result, compared with the conventional gear, the lubrication efficiency of the optimized gear is increased by 41.98%, heat dissipation efficiency increased by 32.21%, and mechanical efficiency loss is decreased by 89.39%, the wear mass is reduced by 74.33%.

Continuous Drive Friction Welded Al/Cu Joints Produced Using Short Welding Time, Elevated Rotational Speed, and High Welding Pressures.

The present study aimed to enhance the efficiency and efficacy of the Al/Cu joint production process implemented by the company VEMID Ltd., Jagodina, Serbia, by attaining sound joints within a very short welding time. For this purpose, the present study aimed at investigating the accuracy and the quality of the continuous drive friction welding (CDFW) process, as well as the optimum combination of CDFW parameters with highest joint efficiency in terms of investigated properties. The accuracy was estimated through an analysis of temperature-time curves recorded during CDFW using an infrared camera. The quality was evaluated through an investigation of the properties of Al/Cu joints produced using different friction (66.7, 88.9, and 133.3 MPa) and forging (88.9, 222.2, and 355.6 MPa) pressures and a constant total welding time (4 s) and rotational speed (2100 rpm). Thermal imaging with an infrared camera demonstrated that the actual total welding time was 15% longer compared to the nominal value. This was attributed to the slow pressure response of the pneumatic brake system. The relative changes in the maximum surface temperature (TMS) during the CDFW process corresponded to changes in welding pressures, indicating the potential of the thermal imaging method for monitoring and assessing this process. A preliminary investigation demonstrated that Al/Cu joints produced using welding pressures less than 88.9 MPa often displayed the presence of non-joined micro-regions at the Al/Cu interface and a significant thickness of interfacial Al2Cu (up to 1 µm). However, when friction pressure was set at 66.7 MPa, an increase in the forging pressure to 222.2 MPa eliminated the presence of non-joined micro-regions and reduced the thickness of Al2Cu to 0.5 µm on the average level. These Al/Cu joints achieved the highest joint efficiencies in terms of strength (100%) and ductility (61%). They exhibited an electrical conductivity higher than 92% of the theoretical value. A further increase in any welding pressure produced similar or deteriorated properties, accompanied by an increase in the consumption of raw materials and energy. Such turn of events was counterproductive to the original goal of increasing the efficiency and efficacy of the CDFW process.

Optimization of Variational Mode Decomposition-Convolutional Neural Network-Bidirectional Long Short Term Memory Rolling Bearing Fault Diagnosis Model Based on Improved Dung Beetle Optimizer Algorithm

(1) Background: Rolling bearings are important components in mechanical equipment, but they are also components with a high failure rate. Once a malfunction occurs, it will cause mechanical equipment to malfunction and may even affect personnel safety. Therefore, studying the fault diagnosis methods for rolling bearings is of great significance and is also a current research hotspot and frontier. However, the vibration signals of rolling bearings usually exhibit nonlinear and non-stationary characteristics, and are easily affected by industrial environmental noise, making it difficult to accurately diagnose bearing faults. (2) Methods: Therefore, this article proposes a rolling bearing fault diagnosis model based on an improved dung beetle optimizer (DBO) algorithm-optimized variational mode decomposition-convolutional neural network-bidirectional long short-term memory (VMD-CNN-BiLSTM). Firstly, an improved DBO algorithm named CSADBO is proposed by integrating multiple strategies such as chaotic mapping and cooperative search. Secondly, the optimal parameter combination of VMD was adaptively determined through the CSADBO algorithm, and the optimized VMD algorithm was used to perform modal decomposition on the bearing vibration signal. Then, CNN-BiLSTM was used as the model for fault classification, and hyperparameters of the model were optimized using the CSADBO algorithm. (3) Results: Finally, multiple experiments were conducted on the bearing dataset of Case Western Reserve University, and the proposed method achieved an average diagnostic accuracy of 99.6%. (4) Conclusions: Experimental comparisons were made with other models to verify the effectiveness of the proposed model. The experimental results show that the proposed model based on an improved DBO algorithm optimized VMD-CNN-BiLSTM can effectively be used for rolling bearing fault diagnosis, with high diagnostic accuracy, and can provide a theoretical reference for other related fault diagnosis problems.

Numerical and experimental investigation of the effect of heat input on weld bead geometry and stresses in laser welding

Abstract Nowadays, laser welding is a powerful joining method. Thanks to the advantages it has, its usage area is increasing day by day. However, getting the desired result from the laser welding process is possible with the proper welding parameter selections. Otherwise, many problems may be encountered, including significantly incomplete penetration. For this reason, parameter selection has been discussed in many studies in the literature. At this point, validated numerical simulation models are precious. Since these models reduce experiment costs and save time. Especially numerical simulation of the structural steel, which is the one of most used materials, is crucial. In this study, the effects of laser power (LP) and welding speed (WS), which are among the vital parameters of laser welding, on weld width and stress were investigated numerically and statistically. Structural steel was selected as the material, and the Taguchi method was carried out for the simulation case study design. Simufact Welding software was used for simulation studies, and simulations were carried out thermomechanical. Thus, more realistic results were obtained via the thermomechanical method. One of the simulation results was verified through an experimental study. The results were evaluated with signal-to-noise (S/N) ratio and a statistical analysis of variance (ANOVA), and as a result of the study, it was seen that the welding speed was a more effective parameter, the optimal parameter combination was found to be 3500 W for laser power and 40 mm/s for welding speed to get maximum weld width and minimum equivalent stress. In addition, it was observed that correctly created simulation studies may provide very close results to experimental studies.

Optimizing a dew point evaporative cooler in data center applications

Abstract The escalating energy consumption of data centers has led to a pressing need for energy-efficient cooling solutions. This paper presents a countercurrent dew point evaporative cooler (DPEC) for data center refrigeration. We developed and experimentally validated a numerical model for DPEC, then formulated regression models using the response surface method. These models link eight key design factors, including geometrical and operational factors, to three performance indices: cooling capacity per unit volume, coefficient of performance, and outlet primary air temperature. We assessed the extent of factor influence on these indices. By using these regression models as objective functions, we used the genetic algorithm for design optimization under two climatic conditions, resulting in various optimal parameter combinations. Our findings highlight the strong predictive accuracy of these models. In comparison to the original design, the optimal design achieved an improvement of 104.8%, an increase of 23.9%, and a reduction of 13.8% in the three indices.

Intelligent automatic operational modal analysis

This work presents a novel methodology for the automatic output-only modal identification of linear structures under ambient vibrations named Intelligent Automatic Operational Modal Analysis (i-AOMA). The proposed methodology leverages the Covariance-based Stochastic Subspace (SSI-cov) algorithm for output-only modal parameter identification and encompasses two key phases. In the first phase, the SSI-cov algorithm is executed using quasi-randomly samples of the control parameters. The corresponding stabilization diagrams are next processed via Kernel Density Estimation in order to prepare a training database for the i-AOMA's intelligent core. This is a machine learning technique (namely a Random Forest algorithm) that predicts the optimal combinations of the control parameters involved in the SSI-cov algorithm. The second phase of i-AOMA rests on the iterative generation of quasi-random control parameter samples. If a sample is classified as feasible by the intelligent core of i-AOMA, then the SSI-cov algorithm is applied, while a new sample is considered otherwise. Such iterative procedure stops once a statistical convergence criterion is fulfilled. Hence, ultimate modal estimates are carried out from the stabilization diagrams, and relevant statistics are computed to quantify the effects of the uncertainties attributable to the variability of the control parameters. The entire Python source code for i-AOMA is made freely available through public web repositories. Some applications of the proposed methodology are finally illustrated.

Computational modelling of scleral photocrosslinking: from rat to minipig to human.

Selective scleral crosslinking has been proposed as a novel treatment to increase scleral stiffness to counteract biomechanical changes associated with glaucoma and high myopia. Scleral stiffening has been shown by transpupillary peripapillary scleral photocrosslinking in rats, where the photosensitizer, methylene blue (MB), was injected retrobulbarly and red light initiated crosslinking reactions with collagen. Here, we adapted a computational model previously developed to model this treatment in rat eyes to additionally model MB photocrosslinking in minipigs and humans. Increased tissue length and subsequent diffusion and light penetration limitations were found to be barriers to achieving the same extent of crosslinking as in rats. Per cent inspired O2, injected MB concentration and laser fluence were simultaneously varied to overcome these limitations and used to determine optimal combinations of treatment parameters in rats, minipigs and humans. Increasing these three treatment parameters simultaneously resulted in maximum crosslinking, except in rats, where the highest MB concentrations decreased crosslinking. Additionally, the kinetics and diffusion of photocrosslinking reaction intermediates and unproductive side products were modelled across space and time. The model provides a mechanistic understanding of MB photocrosslinking in scleral tissue and a basis for adapting and screening treatment parameters in larger animal models and, eventually, human eyes.

Towards void-free copper filling of low-aspect-ratio heat dissipation through holes in packaging substrate with high H2SO4 concentration electroplating system

The void-free filling of high-density low aspect ratio (AR≤5) through holes (THs) in the packaging substrate is beneficial for promoting the integration, reliability, and heat dissipation efficiency of high-power electronic devices. It is a challenge to fill the low ARs THs compactly in direct current (DC) electroplating. The copper filling process relies heavily on the additives’ complex interactions and multiple process parameters. Herein, an ideal convection-sensitive electroplating solution formula — 2.34 M H2SO4, 0.24 M Cu2SO4·5H2O, 30 mg/L Cl−, 9 mg/L SH110, 5 mg/L NTBC, and 200 mg/L PEG — that was appropriate for low ARs THs filling was provided by investigate the electrochemical characteristics of additives. The critical electroplating parameters were optimized by thoroughly examining their influence on filling process, including current density 1.5 ASD, solution flow rate 2.2 L/min, and temperature 25 °C. The process window was enlarged apparently by the combination of optimal parameters for simultaneous void-free filling of THs with a large AR range of 0.95–5, dense crystal structure, and high layer quality could be achieved. Subsequently, the low ARs THs array filled by the optimized technology was implemented in the packaging substrate as the heat dissipation channel, which significantly promoted the heat dissipation efficiency.

A study on optimal parameter combinations for austempered ductile iron

Nodular cast iron possesses a spherical graphite shape and exhibits mechanical properties closely resembling those of steel after heat treatment. The austempering method provides a means of enhancing the mechanical properties of nodular cast iron. This study aimed to investigate the optimal parameter combinations for Austempered Ductile Iron (ADI). The experiments involved varying the austenitization temperature and austempering time. Multiple tests were conducted to analyze the effects of austenitization temperature and tempering time on the microstructure, hardness, and impact toughness. The experimental samples were obtained from Y-block nodular cast iron austenitized at 850, 900, and 950°C for 90 min in an electric furnace. Subsequently, they were quenched in a salt bath at a temperature of 350°C and held for 60, 90, and 120 minutes before being cooled to room temperature. Mechanical tests and microstructure observations were performed on both the as-cast sample and after austempering. The microstructure was observed using an optical microscope before and after the etching. The research findings indicated that austempering increases the hardness and impact toughness of nodular cast iron. The highest hardness (46.8 HRC) was achieved from a parameter combination of an austenitization temperature of 850°C and an austempering time of 90 minutes. This particular sample also exhibited a relatively higher impact energy (3.8 J) compared to the others. The results of this study suggest that the austenitization temperature and tempering time exert an influence on the mechanical properties.